Quality of reporting and trends of emergency obstetric and neonatal care indicators: an analysis from Tanzania district health information system data between 2016 and 2020
Bibliographic record
Abstract
BACKGROUND: Routine health facility data provides the opportunity to monitor progress in quality and uptake of health care continuously. Our study aimed to assess the reliability and usefulness of emergency obstetric care data including temporal and regional variations over the past five years in Tanzania Mainland. METHODS: Data were compiled from the routine monthly district reports compiled as part of the health management information systems for 2016-2020. Key indicators for maternal and neonatal care coverage, emergency obstetric and neonatal complications, and interventions indicators were computed. Assessment on reliability and consistency of reports was conducted and compared with annual rates and proportions over time, across the 26 regions in of Tanzania Mainland and by institutional delivery coverage. RESULTS: Facility reporting was near complete with 98% in 2018-2020. Estimated population coverage of institutional births increased by 10% points from 71.2% to 2016 to 81.7% in 2020 in Tanzania Mainland, driven by increased use of dispensaries and health centres compared to hospitals. This trend was more pronounced in regions with lower institutional birth rates. The Caesarean section rate remained stable at around 10% of institutional births. Trends in the occurrence of complications such as antepartum haemorrhage, premature rupture of membranes, pre-eclampsia, eclampsia or post-partum bleeding were consistent over time but at low levels (1% of institutional births). Prophylactic uterotonics were provided to nearly all births while curative uterotonics were reported to be used in less than 10% of post-partum bleeding and retained placenta cases. CONCLUSION: Our results show a mixed picture in terms of usefulness of the District Health Information System(DHIS2) data. Key indicators of institutional delivery and Caesarean section rates were plausible and provide useful information on regional disparities and trends. However, obstetric complications and several interventions were underreported thus diminishing the usefulness of these data for monitoring. Further research is needed on why complications and interventions to address them are not documented reliably.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".